Download or read book 3D Scene and Event Understanding by Joint Spatio temporal Inference and Reasoning written by Yuanlu Xu and published by . This book was released on 2019 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is a challenging yet crucial task to have a comprehensive understanding of human activities and events in the 3D scene. This task involves many many mid-level vision tasks (e.g., detection, tracking, pose estimation, action/interaction recognition) and requires high-level understandings and reasoning about their relations. In this dissertation, we aim to propose a novel and general framework for both mid-level and high-level tasks under this track, towards a better solution for complex 3D scene and event understanding. Specifically, we aim to formulate problems with interpretable representations, enforce high-level constraints with domain knowledge guided grammar, learn models solving multiple tasks jointly, and infer based on spatial, temporal and casual information. We make three major contributions in this dissertation: First, we introduce interpretable representations to incorporate high-level constraints defined by domain knowledge guided grammar. Specifically, we propose: i) Spatial and Temporal Attributed Parse Graph model (ST-APG) encoding compositionality and attribution for multi-view people tracking, enhancing trajectory associations across space and time, ii) Scene-centric Parse Graph to represent a coherent understanding of information obtained from cross-view scenes for multi-view knowledge fusion, iii) Fashion Grammar for constraining configurations of human appearance and clothing in human parsing, iv) Pose Grammar for describing physical and physiological relations among human body parts in human pose estimation, and v) Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's fluent changes and involved activities in tracking interacting objects. Second, we formulate multiple related tasks into a joint learning, inference and reasoning framework for mutual benefits and better configurations, instead of solving each task independently. Specially, we propose: i) a joint parsing framework for iteratively tracking people locations and estimating people attributes, ii) a joint inference framework modeled by deep neural networks for passing messages from direct, top-down and bottom-up directions in the task of human parsing, and iii) a joint reasoning framework to reason object's fluent changes and track the object in videos, iteratively searching for a feasible causal graph structure. Third, we mitigate the problem of data scarcity and data-hungry model learning using a learning-by-synthesis framework. Given limited training samples, we consider either propagate supervisions to unpaired samples or synthesizing virtual samples that minimize discrepancies with the realistic data. Specifically, we develop a pose sample simulator to augment training samples in virtual camera views for the task of 3D pose estimation, which improves our model cross-view generalization ability. There are several interesting properties regarding the proposed frameworks: i) a novel perspective for problem formulation on joint inference and reasoning on space, time and causality, ii) overcoming the drawbacks of lack of interpretability and data hunger for end-to-end deep learning methods. Experiments show that our joint inference and reasoning framework outperforms existing approaches on many tasks and obtains more interpretable results.

Download or read book 3D Scene Understanding with Efficient Spatio temporal Reasoning written by JunYoung Gwak and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and efficient 3D scene understanding could enable embodied agents to safely interact with the physical world in real-time. The key to the remarkable success of computer vision in the last decade owes to the rediscovery of convolutional neural networks. However, this technology does not always directly translate to 3D due to the curse of dimensionality. The size of the data grows cubically with the voxels, and the same level of input resolution and network depth was infeasible compared to that of 2D. Based on the observation that the 3D space is mostly empty, sparse tensors and sparse convolutions stand out as an efficient and effective 3D counterparts to the 2D convolution by exclusively operating on non-empty spaces. Such efficiency gain supports deeper neural networks for higher accuracy in real-time reference speed. To this end, this thesis explores the application of sparse convolution to various 3D scene understanding tasks. This thesis breaks down a holistic 3D scene understanding pipeline into the following subgoals; 1. data collection from 3D reconstruction, 2. semantic segmentation, 3. object detection, and 4. multi-object tracking. With robotics applications in mind, this thesis aims to achieve better performance, scalability, and efficiency in understanding the high-level semantics of the spatio-temporal domain while addressing the unique challenges the sparse data poses. In this thesis, we propose generalized sparse convolution and demonstrate how our method 1. gains efficiency by leveraging the sparseness of the 3D point cloud, 2. achieves robust performance by utilizing the gained efficiency, 3. makes predictions on empty spaces by dynamically generating points, and 4. jointly solves detection and tracking with spatio-temporal reasoning. Altogether, this thesis proposes an efficient and reliable pipeline for a holistic 3D scene understanding.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Santhanam and published by Springer Nature. This book was released on 2022-05-31 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Download or read book Seeing the World Behind the Image written by Derek Hoiem and published by . This book was released on 2007 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "When humans look at an image, they see not just a pattern of color and texture, but the world behind the image. In the same way, computer vision algorithms must go beyond the pixels and reason about the underlying scene. In this dissertation, we propose methods to recover the basic spatial layout from a single image and begin to investigate its use as a foundation for scene understanding. Our spatial layout is a description of the 3D scene in terms of surfaces, occlusions, camera viewpoint, and objects. We propose a geometric class representation, a coarse categorization of surfaces according to their 3D orientations, and learn appearance-based models of geometry to identify surfaces in an image. These surface estimates serve as a basis for recovering the boundaries and occlusion relationships of prominent objects. We further show that simple reasoning about camera viewpoint and object size in the image allows accurate inference of the viewpoint and greatly improves object detection. Finally, we demonstrate the potential usefulness of our methods in applications to 3D reconstruction, scene synthesis, and robot navigation. Scene understanding from a single image requires strong assumptions about the world. We show that the necessary assumptions can be modeled statistically and learned from training data. Our work demonstrates the importance of robustness through a wide variety of image cues, multiple segmentations, and a general strategy of soft decisions and gradual inference of image structure. Above all, our work manifests the tremendous amount of 3D information that can be gleaned from a single image. Our hope is that this dissertation will inspire others to further explore how computer vision can go beyond pattern recognition and produce an understanding of the environment."

Download or read book Spatio temporal Reasoning for Semantic Scene Understanding and Its Application in Recognition and Prediction of Manipulation Actions in Image Sequences written by Fatemeh Ziaeetabar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human activity understanding has attracted much attention in recent years, due to a key role in a wide range of applications and devices, such as human- computer interfaces, visual surveillance, video indexing, intelligent humanoid robots, ambient intelligence and more. Of particular relevance, performing manipulation actions has a significant importance due to its enormous use, especially for service, as well as industrial robots. These robots strongly benefit from a fast and predictive recognition of manipulation actions. Although, for us as humans performing these actions is a quite triv...

Download or read book Task oriented Visual Understanding for Scenes and Events written by Siyuan Qi and published by . This book was released on 2019 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scene understanding and event understanding of humans correspond to the spatial and temporal aspects of computer vision. Such abilities serve as a foundation for humans to learn and perform tasks in the world we live in, thus motivating a task-oriented representation for machines to interpret observations of this world. Toward the goal of task-oriented scene understanding, I begin this thesis by presenting a human-centric scene synthesis algorithm. Realistic synthesis of indoor scenes is more complicated than neatly aligning objects; the scene needs to be functionally plausible, which requires the machine to understand the tasks that could be performed in the scene. Instead of directly modeling the object-object relationships, the algorithm learns the human-object relations and generate scene configurations by imagining the hidden human factors in the scene. I analyze the realisticity of the synthesized scenes, as well as its usefulness for various computer vision tasks. This framework is useful for backward inference of 3D scenes structures from images in an analysis-by-synthesis fashion; it is also useful for generating data to train various algorithms. Moving forward, I introduce a task-oriented event understanding framework for event parsing, event prediction, and task planning. In the computer vision literature, event understanding usually refers to action recognition from videos, i.e., "what is the action of the person". Task-oriented event understanding goes beyond this definition to find out the underlying driving forces of other agents. It answers questions such as intention recognition ("what is the person trying to achieve"), and intention prediction ("how the person is going to achieve the goal"), from a planning perspective. The core of this framework lies in the temporal representation for tasks that is appropriate for humans, robots, and the transfer between these two. In particular, inspired by natural language modeling, I represent the tasks by stochastic context-free grammars, which are natural choices to capture the semantics of tasks, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs. To overcome this drawback, I generalize the Earley parser to parse sequence data which is neither segmented nor labeled. This generalized Earley parser integrates a grammar parser with a classifier to find the optimal segmentation and labels. It can be used for event parsing, future predictions, as well as incorporating top-down task planning with bottom-up sensor inputs.

Download or read book Inferring the Intentions and Attentions of Agents from Videos written by Dan Xie and published by . This book was released on 2016 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decades, the goal of computer vision, as coined by Marr, is to compute what are where by looking. The paradigm has guided the geometry-based approaches in the 1980s-1990s and appearance-based methods in the past years. Despite of the remarkable progress in recognizing objects, actions, and scenes by using large data sets, better designed features, and machine learning techniques, performances in complex tasks are still far from being satisfactory. One example is the first accident caused by Google's self-driving car in Feb 2016, the accident happened despite the fact that the car's 360-degree sensors likely saw the bus coming, the software made a wrong assumption that the bus behind would yield. Therefore, it can be seen that some complex computer vision tasks cannot be solved by the visible appearance alone. The goal of this thesis is to look for a bigger picture to model and reason the missing dimensions, the mind of agents. By borrowing the powerful concept ``dark matter'' from Physics, we call this area as ``dark vision''. In this thesis, the mind of agents is inferred in spatial and temporal domain jointly. The framework including spatial reasoning in multi-scale space, and temporal reasoning in both observed story in the past and unseen events in the future. 1) Intention means the mind of an agent about the future plan. Dark matter corresponds to entities which are unfeasible to recognize by visual appearances. This includes, not exclusively, i) status of an agent (human, animals or robot)'s goals and intents, like hungry, thirsty, which trigger actions; and ii) attraction relations between an object (like food) and an agent (hungry). Therefore, functional objects can be viewed as ``dark matter'', emanating ``dark energy'' that affects people's trajectories in the video. A Bayesian framework is used to probabilistically model: people's trajectories and intents, constraint map of the scene, and locations of functional objects. 2) Attention represents the mind of an agent at current time. Gaze refers to the location where a person is looking, and attention purpose explains why a person is looking at that location, e.g., to locate a cup. The method in this thesis computes not only human gaze locations in 3D space, but also attention purpose categories in task-driven actions. The human gaze and attention are decomposed into relations among human skeletons, objects, and human gazes in spatial-temporal domain. Such relations are represented by a stochastic graph learned by maximum likelihood estimation in a supervised way. 3) A further step is to discover invisible relations in group activities. This thesis parses low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events. A spatiotemporal And-Or graph framework is proposed to conduct joint inference of the above tasks. This thesis also presents a three-layered And-Or graph to jointly model group activities, individual actions, and participating objects, which not only avoids running a multitude of detectors at all spatiotemporal scales, but also arrives at a holistically consistent video interpretation. Of course, it is well-known that vision is an inverse, ill-posed problem where only the pixels are seen directly and anything else are hidden / latent. The concept of darkness is perpendicular to, and richer than, the meaning of latent / hidden used in vision and probabilistic modeling. It is a measure of the relative difficulty in inferring an entity or relation from the appearance. In computer vision, the literature addresses these dark entities and relations by a lump sum concept: it is context! But what is a formal definition of context? How many types of context are there? How information is passed between entities through context? The literature lacks an explicit and principled framework for joint representation and joint inference. This thesis proposes a framework to explore these ``dark'' dimensions of mind in an explicit manner.

Download or read book Incorporating World Model Knowledge Into Event Parsing Prediction and Reasoning written by Baoxiong Jia and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Event understanding is one of the most fundamental problems in artificial intelligence and computer vision. Rooted in the field of neuroscience, the study and analysis of human motion perception have long suggested that we perceive human activities as goal-directed behaviors. As an essential capability of humans, we interpret others' goals and learn tasks through the endless video stream of daily activities. To endow machines with the same intelligent behaviors, the challenges of emerging such a capability lie in the difficulty of generating a detailed understanding of world model knowledge including situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism in human multi-tasking, and partial observations originated from both the egocentric perception and uncertainties in estimating others' beliefs in multi-agent collaborations. In this dissertation, we propose to study this missing gap from both the data and the modeling perspective by incorporating knowledge of the world model for proper event parsing, prediction, and reasoning. First, we propose three datasets, RAVEN, LEMMA, and EgoTaskQA, to study the event understanding problem from both the abstract and real domain. We further devise three benchmarks to evaluate models' detailed understanding of events with (1) intelligence tests for spatial-temporal reasoning in RAVEN, (2) compositional action recognition and prediction in LEMMA, and (3) task-conditioned question answering in EgoTaskQA. Next, from the modeling side, we decompose the problem of event understanding into a unified framework that involves three essential modules: grounding, inference, and the knowledge base. To properly solve the problem of detailed event understanding, we need to focus on (1) the perception problem for grounding, (2) the knowledge representation problem, and (3) the inference problem. For the perception problem, we discuss the potential in existing models and propose the BO-QSA for the unsupervised emergence of object-centric concepts. For the inference problem, we discuss ways to initialize the overall framework with (1) PrAE which makes use of probabilistic abductions given logical rules, and (2) GEP which leverages stochastic context-free grammars for modeling. We conduct experiments to show their effectiveness on various tasks and also discuss the limitations of each proposed work tohighlight immediate next steps for possible future directions.

Download or read book 2009 IEEE Conference on Computer Vision and Pattern Recognition written by IEEE Staff and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatial Biases in Perception and Cognition written by Timothy L. Hubbard and published by Cambridge University Press. This book was released on 2018-08-23 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerous spatial biases influence navigation, interactions, and preferences in our environment. This volume considers their influences on perception and memory.

Download or read book Compendium of Neurosymbolic Artificial Intelligence written by P. Hitzler and published by IOS Press. This book was released on 2023-08-04 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.

Download or read book Monte Carlo Methods written by Adrian Barbu and published by Springer Nature. This book was released on 2020-02-24 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.

Download or read book Person Re Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.

Download or read book Gaze Following written by Ross Flom and published by Psychology Press. This book was released on 2017-09-25 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: What does a child’s ability to look where another is looking tell us about his or her early cognitive development? What does this ability—or lack thereof—tell us about a child’s language development, understanding of other’s intentions, and the emergence of autism? This volume assembles several years of research on the processing of gaze information and its relationship to early social-cognitive development in infants spanning many age groups. Gaze-Following examines how humans and non-human primates use another individual’s direction of gaze to learn about the world around them. The chapters throughout this volume address development in areas including joint attention, early non-verbal social interactions, language development, and theory of mind understanding. Offering novel insights regarding the significance of gaze-following, the editors present research from a neurological and a behavioral perspective, and compare children with and without pervasive developmental disorders. Scholars in the areas of cognitive development specifically, and developmental science more broadly, as well as clinical psychologists will be interested in the intriguing research presented in this volume.

Download or read book Human Centric Visual Analysis with Deep Learning written by Liang Lin and published by Springer Nature. This book was released on 2019-11-13 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.

Download or read book Visual Object Recognition written by Kristen Grauman and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

Download or read book Applied Spatial Data Analysis with R written by Roger S. Bivand and published by Springer Science & Business Media. This book was released on 2013-06-21 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.